Sales and data go hand in hand these days. Thanks to the wide range of data available, sales teams can now target their prospects more accurately, thereby closing more deals.

Today’s sales intelligence data takes many forms. From customer intent to website analytics to competitor intelligence, there are many datasets sales teams can mine.

Here are four important categories of data sources that every sales team uses to boost revenues.

Customer Lifecycle Data

Successful sales require teams to have deep knowledge of their prospects’ motivations and behavior. From understanding their pain points to the product features they prefer, sales teams must know which talking points to present when interacting with the prospect. Customer lifecycle data helps in multiple ways.

For starters, customer data helps sales teams understand what led to successful deals in the past. Which pain points were solved and how do existing customers use the product to solve them? These signals help sales teams form initial talking points. Customer reviews and questions raised in past interactions also help sales teams anticipate objections from new prospects.

If a company’s solution base is large, customer lifecycle data helps sales teams identify audience segments and cater to their needs specifically. For instance, one segment might be using a product differently from another. In such cases, teams must understand these segments’ respective pain points and target them accordingly.

Matching these audience segments to prospects helps the sales process immensely. Targeting individual prospects with the right offers and pre-empting their issues with helpful content becomes much easier. Aside from helping with prospects, customer lifecycle data also helps companies identify their most profitable markets and seasonal trends in sales.

These trends help them allocate resources efficiently and develop metrics such as customer lifetime value that quantify the monetary amount a customer brings to the company. These data help sales teams better understand their customer base and tailor their pitches accordingly.

Website Analytics Data

Websites are a great way to spot customer intent. They are often the primary points of interaction between a company and its prospects. Everything from the pages browsed to content downloads can point to a prospect’s intent.

Analytics packages offer deep insight into how customers behave these days. One of the most important datasets sales teams can review is the customer’s flow. Which pages did they land on, and where did they proceed from there? Measuring the time spent on a page and correlating that to the pain points an audience segment experiences helps sales teams identify the approach they ought to take with that prospect.

For instance, if a user browses several blog posts and proceeds to download a gated whitepaper, they’re displaying strong intent to evaluate the product against its competition. Sales teams can run email campaigns providing reviews of products and videos demoing the product’s features.

A high bounce rate or low visits to a page also highlight potential problems. Sales teams can collaborate with marketing teams to figure out what the issue is and rectify it. Is the copy inappropriate for some audience segments, or are product images unclear? Does the page need more videos explaining how the product works?

Once teams make changes, they can measure the effectiveness via split tests. Analytics data helps them spot the best version that increases conversions.

Customer Journey Data

Every audience segment has different customer journeys, and sales teams must understand what they look like. For instance, one segment might need multiple approvals from several stakeholders while another might have just one decision-maker.

There are many ways of gathering this data aside from standard lead capture forms, with chatbots, surveys and quiz maker tools offering effective collection methods. Even if you do favor a form interface, though, you can design conditional logic forms that ask questions based on prospect responses. These forms help teams understand the prospect’s issues better. Of course, prospect answers must be correlated to analytics data to determine whether they’re reliable.

In some cases, these answers might reveal flaws in a sales team’s understanding. For instance, if a prospect doesn’t respond within expected time frames after multiple touchpoints, the sales team might assume they’re disinterested. However, the prospect’s decision-making cycle might be longer than the sales team had anticipated. Interviews will reveal this to be the case, and sales teams can adjust their audience metrics accordingly.

Customer journeys are also influenced by the first points of contact a sales team establishes. By measuring conversions per platform, sales teams can better understand which touchpoint assists their customers’ and prospects’ journey the best.

Event-Related Data

Webinars and online events are huge these days and are gradually replacing in-person conferences. These platforms offer sales teams a great way to measure audience engagement and create post-event follow-ups. For instance, a large number of product feature-related questions during a webinar points to a pain point and possible objections during a sales call.

Event engagement also helps sales and marketing teams figure out what was lacking and tailor the next event accordingly. Low engagement can be attributed to any number of reasons, from time of the day to the economic cycle in an industry.

However, teams can measure engagement against benchmark data to determine how successful their event was.

Interpreting Signals to Close More

When combined, all of these datasets help sales teams understand their prospects and craft better experiences. The result is higher revenues and more conversions.